Geochemical caper fingerprints as a tool for geographical

ORIGINAL PAPER

Geochemical caper fingerprints as a tool for geographicalorigin identification

Salvatore Pepi . Alessandro Sardella . Alessandra Bonazza . Carmela Vaccaro

Received: 27 September 2017 / Accepted: 22 December 2017

� Springer Science+Business Media B.V., part of Springer Nature 2018

Abstract The identification of geographical origin

of food products is important for both consumers and

producers to ensure quality and avoid label falsifica-

tions. The caper plant (Capparis spinosa L., Brassi-

cales Capparidaceae), a xerophytic shrub common in

the Mediterranean area, produces buds and fruits that

are commercialized in brine at high price. Those

grown in Italy in the Aeolian Islands are renowned for

their high quality. This study is aimed to establish a

correlation between the geological and geochemical

features of soil and the chemical composition of caper

buds grown in two Aeolian Islands, Lipari and Salina.

Major and trace elements were investigated by X-ray

fluorescence and inductively coupled plasma-mass

spectrometry in soil and caper samples from three

localities in Lipari and Salina, and data from the three

sites were compared by a nonparametric test, a

correlation test and multivariate statistics (principal

component analysis). The results allowed to

discriminate soils according to geolithological char-

acteristics of each area and detect a statistically

significant correspondence between soil and caper

samples for the elements Co, Fe, Mg and Rb,

identifying thus possible geochemical caper finger-

prints of origin. These results may also be useful to

protect the high quality of Aeolian caper products by a

suitable ‘‘Made in Italy’’ trademark and avoid falsi-

fications and frauds.

Keywords Major and trace elements � AeolianIslands � Soil � Principal component analysis �Traceability

Introduction

Scientific research for determination and authentica-

tion of geographical origin of food products has

recently acquired relevance in investigations against

frauds for consumer protection, due to scandals about

falsifications that undermined consumer confidence

and increased the attention for geographical origin of

products (Ariyama et al. 2007; Di Giacomo et al. 2007;

Swoboda et al. 2008; Bong et al. 2013; Longobardi

et al. 2015; Ma et al. 2016; Pepi and Vaccaro 2017). In

Europe, high-quality products are marked by specific

designations: protected designations of origin (PDO)

and protected geographical indications (PGI), follow-

ing the rules by European Union Council Regulation

Electronic supplementary material The online version ofthis article (https://doi.org/10.1007/s10653-017-0063-y) con-tains supplementary material, which is available to authorizedusers.

S. Pepi (&) � C. VaccaroDepartment of Physics and Earth Sciences, University of

Ferrara, via Saragat 1, 44121 Ferrara, Italy

e-mail: [email protected]

A. Sardella � A. Bonazza � C. VaccaroInstitute of Atmospheric Sciences and Climate ISAC-

CNR Bologna, via P. Gobetti 101, 40129 Bologna, Italy

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Environ Geochem Health

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(EEC) No. 2081/1992 and Regulation (EC) No.

178/2002 (Gonzalvez et al. 2000; De la Guardia and

Gonzalvez 2013). The above specific designations

refer to precise geographical areas, establishing an

exclusive link between the place of production and all

products coming from that area (Mantrov 2014). In

order to protect the quality standards of food produc-

tion certified by specific designations and prevent

frauds, the European Union reinforced national con-

trol activities, encouraging scientific identification of

markers of geographical origin that could ensure

authenticity and geographical traceability.

Advances in methods and analytical techniques led

to an increase in application of fingerprinting analysis

of foods for identification of geographical origin

(Gonzalvez et al. 2000; Bandoniene et al. 2013; De la

Guardia and Gonzalvez 2013). Since in organic

material the inorganic component is more stable than

the organic one, several studies examined trace

elements, suggesting the potential application for

determination of geographical origin (Almeida and

Vasconcelos 2003; Thiel et al. 2004; Fabiani et al.

2010; Furia et al. 2011; Zhao et al. 2011; Aceto et al.

2013; Versari et al. 2014). Other authors focused on

geochemical characterization of food products asso-

ciated with a specific geographical environment for

prevention of fraudulent labelling (Nikkarinen and

Mertanen 2004; Censi et al. 2014; Mercurio et al.

2014; Protano and Rossi 2014; Pepi et al.

2016, 2017a, b; Pii et al. 2017).

The studies on territoriality are based on the

hypothesis that chemical elements detected in plants

and in their products reflect those contained in the soil

(Wilson 1998; Thiel et al. 2004; Van Leeuwen and

Seguin 2006; Protano and Rossi 2014; D’Antone et al.

2017; Pepi et al. 2017b). Within these studies, the

geographical features of the production area, such as

the soil type and the climate, are considered relevant

factors affecting the specific designations (Ariyama

et al. 2007; Di Giacomo et al. 2007; Swoboda et al.

2008; Camargo et al. 2010; Lo Feudo et al. 2010;

Adamo et al. 2012; Bong et al. 2013; Liu et al. 2014;

Longobardi et al. 2015; Ma et al. 2016).

The ‘‘Made in Italy’’ trademark identifies food

products of very high quality, due to the highly

favourable climate and agronomic conditions of the

Italian territory. Besides the standardized analyses

routinely performed on these products, an accurate

determination of geographical origin would be

necessary to guarantee the quality and territoriality

of the products ‘‘Made in Italy’’ and to unmask

falsifications.

Capers and caper berries are among the most

renowned Italian products in the world: these are,

respectively, the edible flower buds and fruits of the

caper plant, which species is commonly referred as

Capparis spinosa L. (Brassicales Capparaceae) (Ino-

cencio et al. 2002, 2005). The caper plant is a

deciduous species with fleshy leaves and large white

or pinkish flowers, requiring a semi-arid climate, high

temperatures and strong winds. The young flower buds

and the fruits are edible, and other parts of plants are

used for the production of medicines, cosmetics and

insecticides (Ozcan 1999; Sozzi 2001; Rivera et al.

2003; Sozzi et al. 2012). The edible buds and fruits are

usually commercialized in brine for food industry, at

high price. About 10,000 tons are produced annually

worldwide in 60 countries: in the Mediterranean areas,

the main producers are Spain, Morocco, Turkey and

the Italian islands of Pantelleria and the Aeolian

Archipelago (Ozcan 1999). The Aeolian Islands are

known for the high quality of caper production due to

favourable environmental conditions such as the

Mediterranean climate, the volcanic soil and the

exposure to wind and sea spray (Barbera and Di

Lorenzo 1982), which define the territoriality of this

product.

The purpose of our study was to establish a method

to identify the geographical origin of caper buds

grown in two of the Aeolian Islands, Lipari and Salina,

by correlating geological features and geochemistry of

soil with chemical composition of caper buds. A

detailed and reliable geochemical characterization of

capers from Aeolian Islands may also be useful to

protect the high quality of this product by a ‘‘Made in

Italy’’ trademark, in order to avoid possible falsifica-

tions and frauds.

Materials and methods

Geological setting and sampling areas

The Aeolian arc is an archipelago of active and

dormant volcanoes in the Mediterranean area consist-

ing of seven islands, Vulcano, Lipari, Salina, Alicudi,

Filicudi, Panarea and Stromboli, with structural,

volcanological and petrological variations. The main


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active volcanoes are restricted to the central islands

(Lipari and Vulcano) and the eastern ones (Panarea

and Stromboli): under these islands, the north-west-

ward subduction of the Ionian lithospheric slab

beneath the southern Tyrrhenian Sea causes an

extensional stress regime and deep-focus earthquakes.

The Tindari-Letojanni Fault, a first-order lithospheric

fault, divides the western and eastern sectors of the

Aeolian arc, separating the silica-rich magma compo-

sitions of central islands from the potassium-rich ones

of western and eastern islands (Calanchi et al. 2002;

Lucchi et al. 2013a). Our study focused on the islands

of Lipari and Salina, located in the centre of Aeolian

archipelago (Fig. 1). These islands formed between

267 ka and 1400 A.D. through lava flows, scoriaceous

deposits, lava domes and hydro-magmatic pyroclastic

products (Forni et al. 2013; Lucchi et al. 2013a).

The studied areas in Lipari were Lami

(38�29047.2100N, 14�57015.8400E) and Pianogreca

(38�27057.3100N, 14�56012.5500E) (Fig. 1a, b). The

Lami area belongs to the Pomiciazzo Formation,

characterized by large obsidian-rich coulee with well-

developed flow foliation (Bigazzi et al. 2003; Forni

et al. 2013), while the Pianogreca area belongs to the

Pianoconte Formation, composed by lower (70–56 ka)

and intermediate (56–27 ka) brown tuffs. The study

area in Salina was Leni (38�33017.0900N,14�49029.2200E) (Fig. 1c), belonging to recent conti-

nental sediment and characterized by colluvial

deposits produced by erosion of Punta Fontanelle

Fig. 1 Geological map of the Aeolian Islands, Italy (Lucchi et al. 2013a) showing the location of the three studied areas: ‘‘Lami’’ (a),‘‘Pianogreca’’ (b), ‘‘Leni’’ (c)


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Formation, Pianoconte Formation and Serra di Scia-

rato Formation (Lucchi et al. 2013b).

Soil sampling was carried out in the three areas by

an Edelman auger (Eijkelkamp Soil and Water,

Giesbeek, The Netherlands). For each sampling area,

ten caper plants in Pianogreca and 14 in Lami and Leni

were chosen at random and soil samples were

collected around the base of each caper plant at

50 cm of distance and 50 cm of depth. At harvest time,

for each chosen plant in the three sampling areas,

about 200 caper buds were randomly picked and

stored in polyethylene bags at 4 �C.

Sample preparation and analysis

The soil samples were prepared as pressed powder

pellets for X-ray fluorescence (XRF) analysis. The

samples were dried at 105 �C for 24 h to eliminate the

hygroscopic water and ground in an agate mortar

(Laarmann LMMG 100, Roermond, The Nether-

lands). An amount of about 3 g of powder, over a

base of boric acid, was pressed by a hydraulic press to

obtain pellets, which were analysed by a wavelength-

dispersive spectrometer ARL ADVANT’XP (Thermo

Fisher Scientific, Waltham, MA, USA). Simultane-

ously, 0.6 g portions of sample powder were heated

for about 12 h in a muffle at 1000 �C for determining

loss on ignition (LOI).

For inductively coupled plasma-mass spectrometry

(ICP-MS), the soil samples were dried at 105 �C for

24 h and ground in the Laarman agate mortar. An

amount of 0.20 g of powder was placed in a 50-mL

Teflon digestion vessel (Polytetrafluoroethylene),

43 9 60 mm (VWR International, Milan, Italy),

adding 3 mL of HNO3 (65% in distilled water,

Suprapur�, Merck KGaA, Darmstadt, Germany) and

6 mL of HF (40% in distilled water, Suprapur�, Merck

KGaA, Darmstadt, Germany). Themixture was heated

on a hotplate at 180–190 �C for 4–5 h until complete

drying. Afterwards, 3 mL of NHO3 and 3 mL of HF

were added and the mixture was placed on hotplate for

3 h. The dry residue was resuspended in 4 mL of

HNO3 and placed on the hotplate until complete

drying. The residue was finally resuspended in 2 mL

of HNO3.

Caper samples were washed three times with highly

purified Milli-Q� water (resistivity 18.2 MX cm) and

then dried in an oven at 70 �C for 24 h. Once

completely dried, the samples were ground in an herb

grinder (MC3001-Moulinex, Italy). The ground sam-

ples were prepared for ICP-MS as follows: (1) 0.4 g

portion of the sample was placed in a 50-mL Teflon

digestion vessel, adding 4 mL of HNO3 (65% in

distilled water, Suprapur�, Merck KGaA, Darmstadt,

Germany) and 3 mL of H2O2 (37% in distilled water,

Suprapur�, Merck); (2) the mixture was heated on a

hotplate at 120–160 �C for 3 h until complete drying;

(3) the dry residue was resuspended in 3 mL of HNO3

and 2 mL of H2O2 and placed on hotplate until

complete drying. The dried samples were finally

resuspended in 2 mL of HNO3.

The resultant solutions of all samples were trans-

ferred to plastic flasks and made up to 100 mL with

highly purified Milli-Q� water. An internal element

standard composed of Rh, Re, In and Bi was added to

each solution to obtain a final concentration of 10 ppb.

Analytical determinations

Major elements in soil samples were analysed by XRF,

using a wavelength-dispersive spectrometer ARL

ADVANT’XP (Thermo Fisher Scientific). The chem-

ical composition of soils was expressed as a weight

percentage of the following oxides: SiO2, TiO2,

Al2O3, Fe2O3, MnO, MgO, CaO, Na2O, K2O, P2O5.

The accuracy and precision of data were evaluated

using certified international soil standards (JSd-1

stream sediment powder, Geological Survey of Japan,

Tsukuba, Japan). The analyses on international soil

standards were replicated five times, and the detection

limit (expressed in % weight), the accuracy error and

the precision (relative standard deviation, expressed in

%) of the analyses are reported in Table 1.

The chemical elements in soil and caper samples

were determined by ICP-MS using a Thermo Electron

Corporation X series spectrometer (Thermo Fisher

Scientific, Waltham, MA, USA). The elements deter-

mined were Al, B, Ba, Ca, Cd, Ce, Co, Cr, Cu, Dy, Er,

Eu, Fe, Gd, Ho, K, La, Li, Lu, Mg, Mn, Mo, Na, Nb,

Nd, Ni, P, Pb, Pr, Rb, Sm, Sr, Tb, Th, Ti, Tm, U, V, Y,

Yb, Zn, Zr. The accuracy and precision of the ICP-MS

analysis for soil samples were checked by certified

international soil standard (SS-2 Contaminated soil,

SCP science, Baie d’Urfe, Quebec, Canada) while for

caper samples were checked by certified international

plant standard (SRM 1547 Peach Leaves, National

Institute of Standards and Technology, Gaithersburg,

MD, USA). The detection limit was calculated as the


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concentration equivalent to three times the standard

deviation on the background signal. The accuracy,

precision and detection limit of the analysis for soil

samples are reported in Table 2 and those of caper

samples in Table 3.

Statistical analysis

The Kruskal–Wallis nonparametric test (with post hoc

Dunn’s test) was used to establish the differences

among groups in all data, and Pearson’s correlation

test was used to evaluate the correlations in soil and

caper samples. Principal component analysis (PCA)

was chosen as multivariate analysis of all ICP-MS data

of soil and caper samples, in order to reduce the

dimensionality of the matrix and of the data set with

minimal loss of information (Rencher 2002). All data

analyses were carried out by the software XL STAT,

version 2015.5.02 (Addinsoft, Paris, France).

Results and discussion

Geochemical characterization of soil

The data obtained by X-ray fluorescence (XRF) and

inductively coupled plasma-mass spectrometry (ICP-

MS) on geochemical composition of soil samples

collected from the three study areas are reported in

Tables 4 and 5. The data concerning element concen-

trations in soil samples detected by XRF (Table 4) are

Table 1 Detection limit, accuracy error and precision (relative

standard deviation, RSD) of major elements in soil samples

analysed by X-ray fluorescence (XRF)

Detection limit Accuracy error RSD

SiO2 0.05 1.40 0.36

TiO2 0.01 4.41 4.24

Al2O3 0.05 6.65 0.72

Fe2O3 0.10 5.71 0.54

MnO 0.05 2.09 4.86

MgO 0.01 16.0 8.92

CaO 0.04 2.93 0.66

Na2O 0.01 3.81 3.82

K2O 0.01 8.68 1.15

P2O5 0.01 12.6 17.6

Detection limit is expressed in % (w/w), and accuracy error and

RSD are expressed in %


standard deviation, RSD) of major and trace elements in soil

samples are analysed by inductively coupled plasma-mass

spectrometry (ICP-MS)


Al 1.5 5.6 8.6

Ca 2.0 9.5 15

Fe 1.5 16 13

K 0.1 8.8 16

Mg 0.1 5.9 11

Na 0.1 4.4 13

P 1.5 13 17

Ti 1.0 5.4 8.4

B 40 18 31

Ba 12 10 9.1

Cd 28 7.0 18

Ce 0.5 2.9 3.4

Co 2.5 3.2 8.9

Cr 11 12 11

Cu 33 13 4.6

Dy 0.5 6.9 1.7

Er 0.5 3.8 1.7

Eu 0.5 2.4 10

Gd 0.5 1.4 3.8

Ho 0.5 3.0 3.3

La 0.5 1.2 2.7

Li 1.5 7.5 8.9

Lu 0.5 0.4 5.1

Mn 21 6.5 9.4

Mo 1.5 15 6.7

Nb 15 2.8 10

Nd 0.5 2.9 1.5

Ni 33 6.1 11

Pb 10 15 5.5

Pr 0.5 2.6 1.9

Rb 1.5 1.5 1.6

Sm 0.5 3.9 1.4

Sr 7.0 1.0 5.8

Tb 0.5 1.8 3.2

Th 10 2.8 10

Tm 0.5 7.1 4.7

U 0.5 9.4 6.7

V 26 7.7 17

Y 0.5 1.8 4.6

Yb 0.5 17 1.7

Zn 33 20 7.3


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in agreement with those obtained by ICP-MS

(Table 5) for the same samples. Statistically signifi-

cant differences (p\ 0.05) were obtained for major

elements and trace elements in the soils of the different

areas under study. The highest concentration value for

oxides and for all soil samples was SiO2, followed by

Al2O3, Fe2O3, CaO and K2O. Examining the differ-

ences in oxide concentrations among sampling areas,

SiO2 and K2O resulted much higher in Lami and

Pianogreca in comparison with Leni, whereas Al2O3,

Fe2O3, CaO were higher in Leni. Concerning trace

elements, the highest concentration value for all

samples was Sr, followed by Ba, Rb, V, Zr and Cu.

The highest concentrations of Ba and Rb were

detected in Pianogreca, while the major amount of

Sr was found in Leni.

The correlations among all elements in soil samples

from Lami, Leni and Pianogreca were calculated by

Pearson’s correlation test (Appendix Table A.1 in

Supplementary material). All elements in soil samples

showed significant positive and negative correlations

(p\ 0.05), except U for Leni and Pb for Pianogreca.

In detail, a significant positive correlation of Mg

versus Ca was detected in Lami, Leni and Pianogreca,

whereas a significant positive correlation of Fe versus

V was detected only in Lami and Pianogreca (Ap-

pendix Table A.1 in Supplementary material). These

data suggest different concentrations of these elements

according to geographical origin of soils.

Total alkali silica (TAS) and K2O versus SiO2

diagrams are shown in Appendix Fig. A.1 in Supple-

mentary material (Ewart 1982; Le Bas et al. 1986).

According to TAS diagram, Lami samples belong to

andesite and dacite fields, Leni samples to basaltic

andesite and Pianogreca to dacite–trachydacite (Ap-

pendix Fig. A.1a in Supplementary material). The

K2O versus SiO2 diagram shows a wide variability of

K2O content (Appendix Fig. A.1b in Supplementary

material). Sample soils from Lami and Pianogreca

belong to high-K andesite–dacite (HKAD) field, while

soils from Leni belong to high-K basaltic-andesite

(HKBA) field. The different composition of soils

Table 2 continued


Zr 5.0 4.5 5.2

Detection limit from Al to Ti is expressed in mg/kg and from B

to Zr in lg/kg. Accuracy error and RSD are expressed in %


standard deviation, RSD) of major and trace elements in caper

samples analysed by inductively coupled plasma-mass spec-

trometry (ICP-MS)


Al 0.75 12 20

Ca 1.00 0.3 13

Fe 0.75 8.5 19

K 0.05 3.4 14

Mg 0.03 12 16

Na 0.03 3.6 10

P 0.75 1.6 12

Ti 0.50 5.6 17

B 20.2 15 12

Ba 6.00 0.8 19

Cd 1.43 11 11

Ce 0.25 0.9 9.0

Co 1.25 14 14

Cr 5.50 13 19

Cu 10.5 7.4 11

Dy 0.25 7.8 4.0

Er 0.06 5.6 5.2

Eu 0.13 13 6.5

Gd 0.25 4.0 12

Ho 0.08 5.3 11

La 0.25 8.1 11

Li 0.75 5.8 17

Mn 10.7 2.1 13

Mo 0.75 14 16

Nb 1.08 3.8 10

Nd 0.25 0.5 3.8

Ni 16.5 12 13

Pb 5.25 13 5.5

Pr 0.25 2.4 5.6

Rb 0.75 10 13

Sm 0.13 6.7 2.8

Sr 3.50 3.7 12

Tb 0.01 16 7.0

Th 5.00 2.2 9.4

U 0.10 13 19

V 11.3 12 13

Y 0.10 1.6 5.5

Yb 0.10 7.1 4.4

Zn 16.5 0.3 10

Zr 1.25 3.5 15

Detection limit from Al to Ti is expressed in mg/kg and from B

to Zr in lg/kg. Accuracy error and RSD are expressed in %


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supports their origin from andesite–dacite rocks in

Lipari and basaltic andesite in Salina (Calanchi et al.

2002; Lanzo et al. 2010; Cicchino et al. 2011; Lucchi

et al. 2013a).

Bivariate plots of abundance of major elements

versus SiO2 (Fig. 2) were used to investigate the

mineralogical assemblages in soil composition

(Table 4). The Lami and Pianogreca samples showed

a significant negative correlation with SiO2 of TiO2,

Al2O3, CaO, Fe2O3 and MgO, and a significant

positive correlation of Na2O. The same samples

showed a significant negative correlation with CaO

of Na2O and K2O. The Leni samples showed a

significant negative correlation of Al2O3 with SiO2,

and a significant negative correlation with CaO of

Na2O and K2O (Fig. 2). The negative correlation of

TiO2, Al2O3, Fe2O3 andMgO versus SiO2 in Lami and

Pianogreca samples had a well-defined regression line

(Fig. 2a–d), suggesting the presence of mafic minerals

(orthopyroxene, pyroxene and hornblende), clay min-

erals and/or hydroxides (Calanchi et al. 2002; Lucchi

et al. 2013a, b). In the same samples, the well-defined

regression lines of CaO and Na2O versus SiO2 and the

high concentration of CaO in Leni (Fig. 2e, f) suggest

the presence of sialic minerals (Na and Ca plagioclase)

and clay minerals (Cicchino et al. 2011; Lucchi et al.

2013a).

Table 4 Median concentrations of major elements in soil

samples of the three studied areas analysed by X-ray fluores-

cence (XRF)

Lami Leni Pianogreca p value

SiO2 67.3 ± 2.6 55.0 ± 0.3 65.1 ± 1.4 ***

TiO2 0.37 ± 0.1 0.76 ± 0.0 0.40 ± 0.1 ***

Al2O3 14.3 ± 0.6 17.4 ± 0.2 15.0 ± 0.3 ***

Fe2O3 5.07 ± 1.0 10.1 ± 0.3 5.59 ± 0.7 ***

MnO 0.12 ± 0.0 0.17 ± 0.0 0.12 ± 0.0 ***

MgO 1.16 ± 0.6 3.36 ± 0.1 1.02 ± 0.3 ***

CaO 4.03 ± 1.0 8.92 ± 0.2 2.63 ± 0.4 ***

Na2O 3.35 ± 0.2 2.33 ± 0.1 2.23 ± 0.2 ***

K2O 4.10 ± 0.5 1.77 ± 0.1 4.55 ± 0.2 ***

P2O5 0.17 ± 0.0 0.19 ± 0.1 0.20 ± 0.0 **

A nonparametric multiple test (Test di Kruskal–Wallis) was

applied. Major elements are expressed in % (w/w).

Experimental values correspond to the median ± standard

deviation (SD)

ns not significant

p values **\ 0.01; ***\ 0.001

Table 5 Median concentrations of major and trace elements in

soil samples of the three studied areas analysed by inductively

coupled plasma-mass spectrometry (ICP-MS)


Al 6.32 ± 1.33 7.39 ± 0.94 5.51 ± 0.80 **

Ca 2.41 ± 0.59 3.95 ± 0.61 1.94 ± 0.27 ***

Fe 3.45 ± 0.74 6.12 ± 0.63 3.36 ± 0.37 ***

K 2.44 ± 0.74 0.96 ± 0.08 3.08 ± 0.20 ***

Mg 0.61 ± 0.17 0.86 ± 0.06 0.61 ± 0.08 ***

Na 1.51 ± 0.41 0.90 ± 0.04 1.19 ± 0.13 ***

P 0.05 ± 0.01 0.09 ± 0.02 0.08 ± 0.01 ***

Ti 0.21 ± 0.05 0.40 ± 0.04 0.24 ± 0.03 ***

Mn 0.08 ± 0.01 0.12 ± 0.01 0.08 ± 0.01 ***

B 106 ± 31 19.8 ± 1.9 109 ± 10 ***

Ba 291 ± 63 437 ± 64 489 ± 66 ***

Cd 4.03 ± 1.04 2.43 ± 0.45 5.66 ± 0.21 ***

Ce 60.3 ± 15.4 39.4 ± 8.7 111 ± 8.0 ***

Co 14.3 ± 2.06 26.6 ± 2.2 16.7 ± 2.5 ***

Cr 20.2 ± 4.0 19.8 ± 2.7 38.9 ± 8.9 ***

Cu 63.6 ± 18 135 ± 15 101 ± 16 ***

Dy 3.41 ± 0.91 2.72 ± 0.55 4.71 ± 0.35 ***

Er 2.14 ± 0.58 1.57 ± 0.33 2.78 ± 0.21 ***

Eu 0.60 ± 0.12 1.06 ± 0.19 1.08 ± 0.19 ***

Gd 4.13 ± 1.12 3.54 ± 0.73 7.02 ± 0.56 ***

Ho 0.75 ± 0.20 0.58 ± 0.12 0.98 ± 0.07 ***

La 27.6 ± 9.1 21.9 ± 5.8 69.1 ± 6.8 ***

Li 45.1 ± 12.2 8.04 ± 1.00 44.8 ± 3.9 ***

Lu 0.37 ± 0.11 0.24 ± 0.06 0.44 ± 0.04 ***

Mo 3.82 ± 1.13 1.33 ± 0.14 4.36 ± 0.41 ***

Nb 23.1 ± 6.6 8.58 ± 3.75 18.1 ± 1.8 ***

Nd 22.3 ± 6.4 18.5 ± 4.2 42.4 ± 4.0 ***

Ni 14.7 ± 2.1 19.0 ± 2.1 11.8 ± 1.8 ***

Pb 20.3 ± 4.0 26.9 ± 2.9 29.3 ± 3.2 ***

Pr 6.17 ± 1.9 4.50 ± 1.09 11.9 ± 1.1 ***

Rb 151 ± 44 29.0 ± 5.7 166 ± 15 ***

Sm 4.48 ± 1.14 3.79 ± 0.80 7.52 ± 0.63 ***

Sr 333 ± 107 670 ± 61 560 ± 112 ***

Tb 0.67 ± 0.17 0.55 ± 0.11 0.99 ± 0.08 ***

Th 23.7 ± 8.4 5.59 ± 3.89 49.8 ± 4.1 ***

Tm 0.39 ± 0.11 0.26 ± 0.06 0.47 ± 0.04 ***

U 10.4 ± 1.6 7.16 ± 0.92 9.84 ± 1.13 **

V 164 ± 44 296 ± 46 121 ± 17 ***

Y 18.2 ± 7.9 14.1 ± 3.7 31.6 ± 3.6 ***

Yb 2.36 ± 0.67 1.56 ± 0.35 2.84 ± 0.24 ***

Zn 70.5 ± 12.4 108 ± 5.6 77.3 ± 7.0 ***


123

The scatter plots of Na2O and K2O versus CaO

(Fig. 2g, h) showed significant negative correlations

for Lami and Pianogreca samples. A significant

positive correlation of Na2O versus CaO was detected

for Leni (Fig. 2g) but no significant correlation was

observed for the same samples in scatter plot of K2O

versus CaO (Fig. 2h). These results suggest the

presence of calcium and sodium plagioclase in soils

of Leni samples.

The scatter plots of Ca and Eu versus Mg, of Co

versus Fe and Zr versus Nb for all soil samples

(Table 5) are shown in Fig. 3. Significant positive

correlations were detected for all soil samples in all

scatter plots, except for Eu versus Mg (Fig. 3b).

However, within these significant positive correla-

tions, a clear separation due to different geochemical

and mineralogical composition is evident in Lami and

Pianogreca samples, from Lipari Island, and those of

Leni, from Salina Island (Fig. 3a, c, d). The same

separation is also evident in the scatter plot of Eu

versus Mg (Fig. 3b), where no significant correlation

was detected.

Overall, the data suggest that the variations of

element composition in the investigated soils are

essentially dependent on geochemical processes

occurring in the two islands.

Rare earth elements distribution in soil and caper

samples

Chemical composition of rare earth elements (REE) in

soil and caper samples collected from each area is,

respectively, reported in Tables 5 and 6. Statistically

significant differences (p\ 0.05) in soil samples were

obtained for all elements and study areas (Table 5).

The highest REE values in soil were in Pianogreca,

followed by Lami and Leni, respectively. Correlations

among REE were calculated for soil samples in all

study areas: all showed significant positive correla-

tions (p\ 0.05), except Eu for Lami (Appendix

Table A.1 in Supplementary material). The REE

concentrations in soil samples were normalized

according to chondrite concentrations (McDonough

and Sun 1995), and the distribution patterns are

reported in Fig. 4a. These patterns were characterized

by a progressive decrease in chondrite-normalized

REE concentrations along the series and by negative

anomalies of Eu in Pianogreca and Lami (Fig. 4a).

The mobility of REE in soil is linked to chemical

availability, organic matter, pH, fertilizers, redox

potential conditions and texture (Kabata-Pendias

2011; Aide and Aide 2012; Pepi et al. 2016). The Eu

negative anomalies in soil (Fig. 4a) are probably

linked to fractionation of plagioclase when Ca

replaces Eu (Censi et al. 2014; Pepi et al. 2016): the

Eu anomaly allows to identify the petrogenetic

processes of Lami and Pianogreca in Lipari in

comparison with those of Leni in Salina.

Concerning REE distribution in caper samples, the

highest values were found in Leni, followed by Lami

and Pianogreca, respectively (Table 6). The correla-

tions among REE for caper samples were calculated in

the investigated areas of the present study. All REE

values were significantly different, either positively or

negatively (p\ 0.05), except Ce and Pr in Lami and

La in Leni (Appendix Table A.2 in Supplementary

material). The REE concentrations in caper samples

were normalized according to chondrite concentra-

tions (McDonough and Sun 1995), and the distribution

patterns are reported in Fig. 4b. In this case, the REE

patterns were characterized by a progressive decrease

in chondrite-normalized concentrations along the

series, together with Eu positive anomalies and Sm

and Gd negative anomalies in both Lami and

Pianogreca (Fig. 4b). As in soil samples, the Eu

positive anomaly may be related to the physiological

ability of this rare element to replace Ca2? in aerial

parts of the plant (Zeng et al. 2003), binding to proteins

in a stronger way in comparison with Ca2? (Kruk et al.

2003). The Gd and Sm negative anomalies suggest an

environmental pollution due to an anthropogenic

contamination of water used for irrigation (Brioschi

et al. 2013; Merschel et al. 2015). Overall, these REE

anomalies in caper samples apparently reflect those of

the respective growth areas. In Leni, lower REE

concentrations were found in soil than in caper

Table 5 continued


Zr 165 ± 36 102 ± 20 146 ± 11 ***


applied. Major elements from Al to Mn are expressed in % (w/

w), and trace elements from B to Zr are expressed in mg/kg.

Experimental values correspond to the median ± standard

deviation (SD)

ns not significant

p values **\ 0.01; ***\ 0.001


123

Fig. 2 Bivariate plots of

abundances of SiO2 versus

TiO2 (a), Al2O3 (b) Fe2O3,

(c), MgO (d) CaO (e) Na2O(f) and of CaO versus Na2O

(g) and K2O (h), in soil

samples from Lami, Leni

and Pianogreca. Values of

oxides are expressed in %

(w/w)


123

samples, suggesting a higher bioavailability of REE in

soil in this study area. The contrary was observed in

Lami and Pianogreca, suggesting thus a lower

bioavailability of REE in soil in comparison with

Leni. These results suggest that the lower bioavail-

ability of REE in Lami and Pianogreca may be due to

the andesite–dacite parent material of these areas in

comparison with the basaltic-andesite parent material

of Leni soil. These differences may therefore be used

to geochemically discriminate capers grown on sub-

strates from different areas, tracing thus the geograph-

ical origin of products (Censi et al. 2014).

Chemical composition in caper samples

Major and trace elements concentrations in caper

samples from the three studied areas in Lipari and

Salina are listed in Table 6. Statistically significant

differences (p\ 0.05) were obtained for all major and

trace elements except Cu. For major elements, the

highest concentration in all areas was that of K,

followed by P, Ca, Mg and Na. For trace elements, the

highest concentration was that of Rb, followed by Zn,

Fe, Ti, B, Sr, Mn, Ba, Al andMo: however, in Leni, the

Ba concentration was higher than Al (Table 6).

Correlations among major and trace elements in caper

samples were also calculated for all study areas: all

Fig. 3 Bivariate plots of major and trace elements of Mg versus Ca (a), Mg versus Eu (b), Fe versus Co (c) and Nb versus Zr (d) in soilsamples from Lami, Leni and Pianogreca. Values of major elements are expressed in % (w/w) and values of trace elements in mg/kg


123

elements in caper samples showed significant positive

and negative correlations (p\ 0.05), except Nb in

Lami and Nb and U in Pianogreca (Appendix

Table A.2 in Supplementary material). Comparing

these data, a significant positive correlation of K

versus Mg was detected in Pianogreca, but no

correlations between these elements appeared in Lami

and Leni: these data suggest a different mobility of

these elements in the different areas.

The content values of major and trace elements in

caper samples shown in Table 6 were plotted in Fig. 5

together with linear regression values. A positive

correlation was detected for Ca versusMg (Fig. 5a), in

accordance with high contents of Ca and Mg and

higher bioavailability of Ca and Mg in all study areas

(Morard et al. 1996; Navarro et al. 2000; Kotschau

et al. 2014), related to the abundance of primary

minerals such as augite, hornblende and the feldspar

plagioclase (Troeh and Thompson 1993; Barker and

Pilbeam 2007; Kabata-Pendias 2011).

A well-defined positive linear regression of Sr

versus Ba (Fig. 5b) was observed in all the investi-

gated areas. Previous studies supported the close

relationship and the synergy between Sr and Ba,

according to the plant metabolic requirements (Ka-

bata-Pendias 2011). Concerning Rb versus Fe

(Fig. 5c), no significant correlation was detected, but

these two elements clearly showed different rates for

the three studied areas, suggesting a different bioac-

cumulation of Rb and Fe with respect to geographical

origin.

No significant correlation was also observed for Ca

versus V (Fig. 5d) but some separation of rates

according to geographical origin could be detected:

this suggests a different uptake and accumulation of V

in caper plants according to the concentration of this

element in the investigated areas.

Table 6 Median concentrations of major and trace elements in

caper samples of the three studied areas analysed by ICP-MS


Ca 0.46 ± 0.07 0.66 ± 0.12 0.25 ± 0.04 ***

K 3.35 ± 0.22 3.06 ± 0.37 2.62 ± 0.23 ***

Mg 0.26 ± 0.03 0.29 ± 0.02 0.24 ± 0.03 ***

P 0.35 ± 0.02 0.36 ± 0.04 0.46 ± 0.04 ***

Al 3.96 ± 0.68 1.57 ± 0.17 3.73 ± 0.14 ***

B 26.8 ± 2.0 31.2 ± 5.4 16.8 ± 1.74 ***

Ba 1.15 ± 0.13 2.88 ± 1.15 2.16 ± 0.39 ***

Cu 16.1 ± 2.0 16.4 ± 3.1 17.4 ± 1.65 n.s

Fe 101 ± 9.4 78.3 ± 5.4 36.7 ± 5.61 ***

Mn 14.6 ± 3.0 22.7 ± 7.4 7.68 ± 0.68 ***

Li 5.60 ± 0.06 0.04 ± 0.02 0.03 ± 0.01 ***

Na 107 ± 38 217 ± 73 106 ± 8.1 **

Ni 1.33 ± 0.32 1.72 ± 0.28 0.84 ± 0.09 ***

Rb 120 ± 19 53.0 ± 13.2 113 ± 6.2 ***

Sr 18.7 ± 3.1 33.5 ± 9.7 16.4 ± 3.6 ***

Ti 12.6 ± 2.2 13.7 ± 4.1 24.1 ± 1.3 ***

Zn 67.4 ± 11.7 80.1 ± 14.2 46.4 ± 3.6 ***

Cd 45.7 ± 15.8 73.4 ± 23.3 76.7 ± 14.7 **

Ce 21.4 ± 5.3 15.8 ± 3.20 14.6 ± 2.6 n.s

Co 143 ± 34 172 ± 47 23.4 ± 7.27 ***

Cr 81.7 ± 10.9 215 ± 65 206 ± 60 ***

Dy n.d 0.79 ± 0.41 0.32 ± 0.25 ***

Er 0.29 ± 0.07 0.28 ± 0.23 0.23 ± 0.17 *

Eu 0.31 ± 0.19 0.83 ± 0.19 0.63 ± 0.16 ***

Gd 0.30 ± 0.08 1.71 ± 0.63 0.29 ± 0.21 ***

Ho n.d 0.28 ± 0.15 n.d n.s

La 5.29 ± 0.63 7.19 ± 1.53 5.69 ± 1.95 *

Mo 974 ± 113 980 ± 159 749 ± 78 ***

Nb 69.7 ± 28.3 2.27 ± 1.74 2.91 ± 1.92 ***

Nd 2.71 ± 1.87 4.91 ± 1.54 3.65 ± 2.43 *

Pb 30.4 ± 10.5 35.1 ± 21.4 18.1 ± 8.8 **

Pr 1.48 ± 0.26 1.63 ± 0.85 1.69 ± 0.28 n.s

Sm 0.28 ± 0.11 1.38 ± 0.35 0.56 ± 0.16 ***

Tb 0.41 ± 0.34 0.30 ± 0.08 0.09 ± 0.13 **

Th 0.33 ± 0.22 1.71 ± 0.87 2.43 ± 0.94 ***

U 360 ± 239 17.0 ± 25.7 0.51 ± 0.22 ***

V 162 ± 50 230 ± 29 79.0 ± 10.1 ***

Y 0.29 ± 0.02 4.63 ± 1.42 3.20 ± 2.19 ***

Yb 0.31 ± 0.07 0.30 ± 0.19 0.30 ± 0.16 n.s

Table 6 continued


Zr 5.51 ± 2.61 73.8 ± 8.6 18.6 ± 3.82 ***


applied. Major elements from Ca to P are expressed in % (w/

w), and trace elements from Al to Zn are expressed in mg/kg

and from Cd to Zr are expressed in lg/kg. Experimental values

correspond to the median ± standard deviation (SD)

ns not significant

p values *\ 0.05; **\ 0.01; ***\ 0.001


123

Well-defined positive correlations were observed

for V versus Ni in Lami (Fig. 5e), Ni versus Zn in all

areas (Fig. 5f), and Zn versus B (Fig. 5g) and Ti

versus P (Fig. 5h) in Lami and Pianogreca. Concern-

ing the correlations of these trace elements, the first

one supports the data obtained for Ca versus V and the

second one suggests that the absorption of Ni and its

translocation in caper plants are favoured by Zn

(Kaplan et al. 1990; Kabata-Pendias 2011). The

positive correlation between Zn and B suggests that

uptake of B by the plant may increase in the presence

of Zn, because Zn exerts a partial protective effect

against toxic levels of B in rooting zones (Kabata-

Pendias 2011).

The interactions between P and Ti are complex: in

plants, an interference of P ions with Ti mobility has

been previously reported, probably similar to OH

groups reactions (Kabata-Pendias 2011), but no inter-

ference between these two trace elements was detected

in the areas of the present study.

Again, the differences in correlations of major and

trace elements shown in Fig. 5 appear closely related

to soil type and geographical origin, suggesting thus

that these parameters could be useful as geographical

origin markers of Italian caper.

Fig. 4 Concentrations of

rare earth elements

determined by ICP-MS

expressed in mg/kg in soil

samples and lg/g in caper

samples and normalized by

the chondrite values, in soil

(a) and caper (b) samples

from Lami, Leni and

Pianogreca. The

concentrations of Tm and Lu

in caper samples were below

the detection limit of ICP-

MS


123

Fig. 5 Bivariate plots of major and trace elements of Mg versus

Ca (a), Ba versus Sr (b), Fe versus Rb (c) V versus Ca (d), Niversus V (e) Zn versus Ni (f), B versus Zn (g) and P versus Ti

(h) in caper samples from the three studied areas. Values of major

elements are expressed in % (w/w) and values of trace elements

in mg/kg. The coefficient of determination (R2) is indicated


123

Correlations among elements in soil and caper

samples

The results concerning the different contents of

elements in soil and caper samples presented in

Tables 5 and 6 were used to assess the correlations

between these elements and identify possible geo-

chemical features of the soil associated with capers.

The contents of elements in soil were plotted against

those in capers, calculating the linear regression

values. Only four elements (Co, Fe, Mg and Rb)

showed a significant correlation (R2[ 0.60) with an

internal and external 95% confidence interval (Fig. 6).

A significant positive correlation was found for Co and

Mg in Lami (Fig. 6a, d), for Co and Fe in Leni

(Fig. 6b, e) and for Mg and Rb in Pianogreca (Fig. 6c,

f).

The soil–caper positive correlation for Co detected

in Lami and Leni may be related to the interchange-

ability of this element with Mg, Fe andMn, previously

documented in plant tissues (Kabata-Pendias 2011).

Concerning Mg, its capacity to be easily assimilated

by plants from the soil and therefore its correlation

with the soil content have been widely described

(Barker and Pilbeam 2007; Kabata-Pendias 2011;

Kotschau et al. 2014). This element is known to be

present in caper and caperberries in significant con-

tents (Ozcan, 1999). In this study, the content of Mg

appears able to discriminate capers from two different

localities of the same island, Lami and Pianogreca.

A well-defined positive linear regression for Fe

could be due to the high absorption capacity by the

plants. The uptake of Fe in plants and its transport

among plant tissues are related to the chemical species

of Fe that are Fe2?, Fe3? and Fe chelates. The plant

root may introduce Fe3? and reduce it to Fe2?, the

species which is most readily transported in plant

organs (Kabata-Pendias 2011).

The soil–caper positive correlation detected for Rb

in Pianogreca could be explained by the higher content

of this element in volcanic sediments rich in feldspat

minerals (Fig. 6f). The Rb values could also be related

to the interchangeability of this element with K

(Kabata-Pendias 2011).

The results of soil–caper correlations among the

major and trace elements show that Co, Mg, Fe and Rb

are relevant to connect the caper plant to the

geochemical location of the soil, discriminating

among the areas of caper cultivation in Lami, Leni

and Pianogreca, identifying thus its territoriality.

Multivariate analysis

Principal component analysis (PCA) was applied to

soil and caper data (Tables 5, 6) to evaluate geochem-

ical differences derived from geographical origin

(Fig. 7). In soil samples, the model generated by

PCA analysis explained 83.4% of total variance. The

correlation-loading plot of soil samples (Fig. 7a)

showed that the variables B, Ca, Cd, Co, Fe, K, Li,

Mg, Mn, Mo, Nb, Ni, Rb, Th, V, Y and Zr, but not Eu,

were strongly correlated with F1, explaining 63.3% of

variance. The remaining variables (Ba, Eu and P) were

strongly correlated with F2, explaining 20.0% of

variance. In detail, elements such as B, K, Li, Mo, Na,

Nb, Rb and Zr were important in discriminating Lami,

Al, Ca, Co, Mg, Mn, Ni, Ti and V were important in

discriminating Leni and Cd, REE and Y in discrim-

inating Pianogreca.

When the PCA analysis was applied to caper data,

the model generated by the analysis explained 56.6%

of total variance. The correlation-loading plot of caper

samples (Fig. 7b) showed that variables including Al,

Ca, Gd, Mn, Na, Ni, Rb, Sm, V and Zr were strongly

correlated with F1, explaining 32.3% of variance. The

remaining variables, Cr, Fe, K, Li, Nb, Ti and Th, were

strongly correlated with F2, explaining 24.3% of

variance. Elements such as Al, Fe, K, Li and Nb were

thus useful for the discrimination of samples from

Lami, while Ca, Dy, Gd, Na, Sm, Sr and Zr were

important in discriminating samples from Leni and P

and Ti from Pianogreca.

The PCA analysis was also applied to jointly

evaluate the soil and caper data in order to identify

suitable geochemical markers of geographical origin.

The model generated by PCA analysis explained

92.9% of total variance. The correlation-loading plot

of joint data (Fig. 7c) showed that variables Al, Ba,

Ca, Cd, Co, Cr, Fe, Li, Mg, Mo, Mn, Na, Ni, P, Pb, Sr,

Th, Ti, V, Y and Zr were strongly correlated with F1,

explaining 77.0% of variance. The elements K, Rb and

Zn were strongly correlated with F2, explaining 15.9%

of variance.

Overall, the PCA analysis shown in Fig. 7 supports

the hypothesis that an excellent discrimination of

geographical origin is possible for capers grown in

Lami, Leni and Pianogreca.


123

Fig. 6 Linear regression values (continuous line) of concen-

trations of Co (a, b), Mg (c, d), Fe (e) and Rb (f) in soil versus

caper samples, showing a significant correlation (R2[ 0.60)

with an internal (dashed line) and external (dotted line) 95%

confidence interval. The value of Mg is expressed in % (w/w),

and the values of Co, Fe and Rb are expressed in mg/kg


123

Conclusions

Major and trace elements in soil and caper samples

from two locations in Lipari Island, Lami and

Pianogreca, and one in Salina Island, Leni, were

evaluated as possible geographical markers for the

territoriality of products ‘‘Made in Italy’’. The anal-

yses of soil samples by XRF and ICP-MS were able to

geochemically characterize and discriminate the three

investigated areas. In caper samples, the analysis by

ICP-MS confirmed a different accumulation of major

and trace elements in the three locations. The Eu

anomaly detected by ICP-MS in soil samples allowed

to identify the petrogenetic processes occurring in

Lipari Island in comparison with those occurring in

Salina, while Eu, Gd and Sm anomalies detected in

caper samples reflected those of the respective growth

areas, supporting the hypothesis that these parameters

could be used for tracing the origin of products.

The results obtained by ICP-MS show that the

elements strongly correlated from a geochemical point

of view in soil and caper samples are Co, Fe, Mg and

Rb.

The PCA analysis allowed to discriminate soil and

caper samples from Lami, Leni and Pianogreca

according to their geographical origin. The elements

useful as geochemical markers were Al, B, Ca, Co, Fe,

Li, Mg, Mn, Nb, Ni, Rb, Sr, Ti, V and Zr, but among

them, Co, Fe, Mg and Rb were able to establish a

reliable correlation between geolithological features

of soil and the chemical composition of capers.

Overall, the results of the present study confirm that

some major and trace elements could be used as

geochemical markers of capers according to the

geological areas. These elements therefore could be

useful to establish geochemical caper fingerprints

(GCF) for testing the origin of this product and create a

protected designation of origin (PDO) label, in order to

protect the ‘‘Made in Italy’’ trademarks against the

falsifications.

Acknowledgements The authors owe thanks to Renzo

Tassinari for technical advice and experimental support, and

Nunziata Picone, Guglielmo Sardella, Bartolo Cambria and

Salvatore D’Amico for their support in the sampling campaign.

The authors also wish to thank Dr. Milvia Chicca for useful

suggestions in the manuscript.

Fig. 7 Principal component analysis (PCA) plot of the values

of elemental concentrations in soil (a) caper (b) and soil–caper

(c) of Lami, Leni and Pianogreca, shown in Tables 5 and 6. The

factor loading plot is shown on the right side: F1, first

discriminant function; F2, second discriminant function


123

References

Aceto, M., Robotti, E., Oddone, M., Baldizzone, M., Bonifa-

cino, G., Bezzo, G., et al. (2013). A traceability study on the

Moscato wine chain. Food Chemistry, 138, 1914–1922.

Adamo, P., Zampella, M., Quetel, C. R., Aversano, R., Dal Piaz,

F., De Tommasi, N., et al. (2012). Biological and geo-

chemical markers of the geographical origin and genetic

identity of potatoes. Journal Geochemical Exploration,

121, 62–68.

Aide, M. T., & Aide, C. (2012). Rare earth elements: Their

importance in understanding soil genesis. International

Scholarly Research Notices, 2012, 1–11.

Almeida, M., & Vasconcelos, M. T. (2003). Multielement

composition of wines and their precursors including

provenance soil and their potentialities as fingerprints of

wine origin. Journal of Agricultural and Food Chemistry,

51, 4788–4798.

Ariyama, K., Aoyama, Y., Mochizuki, A., Homura, Y., Kado-

kura, M., & Yasui, A. (2007). Determination of the geo-

graphic origin of onions between three main production

areas in Japan and other countries by mineral composition.

Journal of Agricultural and Food Chemistry, 55, 347–354.

Bandoniene, D., Zettl, D., Meisel, T., & Maneiko, M. (2013).

Suitability of elemental fingerprinting for assessing the

geographic origin of pumpkin (Cucurbita pepo var. styri-

aca) seed oil. Food Chemistry, 136, 1533–1542.

Barbera, G., & Di Lorenzo, R. (1982). La coltura specializzata

del cappero nell’isola di Pantelleria. L’informatore

Agrario, 38, 2113–2117.

Barker, A. L., & Pilbeam, D. J. (2007). Handbook of plant

nutrition (1st ed.). Boca Raton, FL: Taylor & Francis

Group, LLC.

Bigazzi, G., Coltelli, M., & Norelli, P. (2003). Nuove eta delle

ossidiane di Lipari determinate con il metodo delle tracce

di fissione. Geoitalia, Bellaria, 48, 444–446.

Bong, Y. S., Song, B. Y., Gautam, M. K., Jang, C. S., An, H. J.,

& Lee, K. S. (2013). Discrimination of the geographic

origin of cabbages. Food Control, 30, 626–630.

Brioschi, L., Steinmann, M., Lucot, E., Pierret, M. C., Stille, P.,

Prunier, J., et al. (2013). Transfer of rare earth elements

(REE) from natural soil to plant systems: Implications for

the environmental availability of anthropogenic REE.

Plant and Soil, 366, 143–163.

Calanchi, N., Peccerillo, A., Tranne, C. A., Lucchini, F., Rossi,

P. L., Kempton, L., et al. (2002). Petrology and geo-

chemistry of volcanic rocks from the island of Panarea:

Implications for mantle evolution beneath the Aeolian

island arc (southern Tyrrhenian sea). Journal of Vol-

canology and Geothermal Research, 115, 367–395.

Camargo, A. B., Resnizky, S., Marchevsky, E. J., & Luco, J. M.

(2010). Use of the Argentinean garlic (Allium sativum L.)

germplasm mineral profile for determining geographic

origin. Journal of Food Composition and Analysis, 23,

586–591.

Censi, P., Saiano, F., Pisciotta, A., & Tuzzolino, N. (2014).

Geochemical behaviour of rare earths in Vitis vinifera

grafted onto different rootstocks and growing on several

soils. Science of the Total Environment, 474, 597–608.

Cicchino, A. M. P., Zanella, E., De Astis, G., Lanza, R., Lucchi,

F., Tranne, C. A., et al. (2011). Rock magnetism and

compositional investigation of Brown Tuffs deposits at

Lipari and Vulcano (Aeolian Islands—Italy). Journal of

Volcanology and Geothermal Research, 208, 23–38.

D’Antone, C., Punturo, R., & Vaccaro, C. (2017). Rare earth

elements distribution in grapevine varieties grown on

volcanic soils: An example fromMount Etna (Sicily, Italy).

Environmental Monitoring and Assessment. https://doi.

org/10.1007/s10661-017-5878-6.

De la Guardia, M., & Gonzalvez, A. (2013). Food protected

designation of origin: Methodologies and applications (1st

ed.). Amsterdam: Elsevier.

Di Giacomo, F., Del Signore, A., & Giacco, M. (2007). Deter-

mining the geographic origin of potatoes using mineral and

trace element content. Journal of Agricultural and Food

Chemistry, 55, 860–866.

Ewart, A. (1982). The mineralogy and petrology of tertiary-

recent orogenic volcanic rocks: With special reference to

the andesitic-basaltic compositional range. In R. S. Thorn

(Ed.), Andesites: Orogenic andesites and related rocks (pp.

25–95). New York, NY: Wiley.

Fabiani, M. P., Arrua, R. C., Vazquez, F., Diaz, M. P., Baron, M.

V., & Wunderlin, D. A. (2010). Evaluation of elemental

profile coupled to chemometrics to assess the geographical

origin of Argentinean wines. Food Chemistry, 119,

372–379.

Forni, F., Lucchi, F., Peccerillo, A., Tranne, C. A., Rossi, P. L.,

& Frezzotti, M. L. (2013). Stratigraphy and geological

evolution of Lipari volcanic complex (central Aeolian

archipelago). In F. Lucchi, A. Peccerillo, J. Keller, C.

A. Tranne, & P. L. Rossi (Eds.), The Aeolian islands vol-

canoes (pp. 213–279). London: Geological Society

Memoirs.

Furia, E., Naccarato, A., Sindona, G., Stabile, G., & Tagarelli,

A. (2011). Multielement fingerprinting as a tool in origin

authentication of PGI food products: Tropea red onion.

Journal of Agricultural and Food Chemistry, 59,

8450–8457.

Gonzalvez, A., Llorens, A., Cervera, M. L., Armenta, S., & De

la Guardia, M. (2000). Elemental fingerprint of wines from

the protected designation of origin Valencia. Food Chem-

istry, 112, 26–34.

Inocencio, C., Alcaraz, F., Calderon, F., Obon, C., & Rivera, D.

(2002). The use of floral characters in Capparis sect. Cap-

paris to determine the botanical and geographical origin of

capers. European Food Research and Technology, 214,

335–339.

Inocencio, C., Cowan, R. S., Alcaraz, F., Rivera, D., & Fay, M.

F. (2005). AFLP fingerprinting in Capparis subgenus

Capparis related to the commercial sources of capers.

Genetic Resources and Crop Evolution, 52, 44–137.

Kabata-Pendias, A. (2011). Trace elements in soils and plants

(4th ed.). Boca Raton, FL: CRC Press.

Kaplan, D. I., Adriano, D. C., Carlson, C. L., & Sajwan, K. S.

(1990). Vanadium: Toxicity and accumulation by beans.

Water, Air, and Soil Pollution, 49, 81–91.

Kotschau, A., Buchel, G., Einax, J. W., Meißner, R., von

Tumpling, W., & Merten, D. (2014). Element pattern

recognition and classification in sunflowers (Helianthus


123

https://doi.org/10.1007/s10661-017-5878-6

https://doi.org/10.1007/s10661-017-5878-6

annuus) grown on contaminated and non-contaminated

soil. Microchemical Journal, 114, 164–174.

Kruk, J., Burda, K., Jemiola-Rzeminska, M., & Strzalka, K.

(2003). The 33 kDa protein of photosystem II is a low

affinity calcium- and lanthanide-binding protein. Bio-

chemical Journal, 42, 14862–14867.

Lanzo, G., Basile, S., Brai, M., & Rizzo, S. (2010). Volcanic

products of Lipari (Aeolian Islands, Italy): Multivariate

analysis of petrographic and radiometric data. Radiation

Measurements, 45, 816–822.

Le Bas, M. J., Le Maitre, R. W., Streckeisen, A., & Zanettin, B.

(1986). A chemical classification of volcanic rocks based

on the total alkali-silica diagram. Journal Petrology, 27,

745–750.

Liu, H. C., You, C. F., Chen, C. Y., Liu, Y. C., & Chung, M. T.

(2014). Geographic determination of coffee beans using

multi-element analysis and isotope ratios of boron and

strontium. Food Chemistry, 142, 439–445.

Lo Feudo, G., Naccarato, A., Sindona, G., & Tagarelli, A.

(2010). Investigating the origin of tomatoes and triple

concentrated tomato pastes through multielement deter-

mination by inductively coupled plasma mass spectrome-

try and statistical analysis. Journal of Agricultural and

Food Chemistry, 58, 3801–3807.

Longobardi, F., Casiello, G., Cortese, M., Perini, M., Camin, F.,

Catucci, L., et al. (2015). Discrimination of geographical

origin of lentils (Lens culinarisMedik.) using isotope ratio

mass spectrometry combined with chemometrics. Food

Chemistry, 188, 343–349.

Lucchi, F., Gertisser, R., Keller, J., Forni, F., De Astis, G., &

Tranne, C. A. (2013a). Eruptive history and magmatic

evolution of the island of Salina (central Aeolian archipe-

lago). In F. Lucchi, A. Peccerillo, J. Keller, C. A. Tranne, &

P. L. Rossi (Eds.), The Aeolian islands volcanoes (pp.

155–211). London: Geological Society Memoirs.

Lucchi, F., Peccerillo, A., Keller, J., Tranne, C. A., & Rossi, P.

L. (2013b). The Aeolian islands volcanoes (1st ed.). Lon-

don: Geological Society Memoir.

Ma, G., Zhang, Y., Zhang, J., Wang, G., Chen, L., Zhang, M.,

et al. (2016). Determining the geographical origin of Chi-

nese green tea by linear discriminant analysis of trace

metals and rare earth elements: Taking Dongting Bilu-

ochun as an example. Food Control, 59, 714–720.

Mantrov, V. (2014). EU Law on indications of geographical

origin theory and practice. Berlin: Springer.

McDonough, W. F., & Sun, S. S. (1995). Composition of earth.

Chemical Geology, 120, 223–253.

Mercurio, M., Grilli, E., Odierna, P., Morra, V., Prohaska, T.,

Coppola, E., et al. (2014). A ‘Geo-Pedo-Fingerprint’ (GPF)

as a tracer to detect univocal parent material-to-wine pro-

duction chain in high quality vineyard districts, Campi

Flegrei (Southern Italy). Geoderma, 231, 64–78.

Merschel, G., Bau, M., Baldewein, L., Dantas, E. L., Walde, D.,

& Buhn, B. (2015). Tracing and tracking wastewater-

derived substances in freshwater lakes and reservoirs:

Anthropogenic gadolinium and geogenic REEs in Lake

Paranoa, Brasilia. Comptes Rendus Geoscience, 347,

284–293.

Morard, P., Pujos, A., Bernadac, A., &Bertoni, G. (1996). Effect

of temporary calcium deficiency on tomato growth and

mineral nutrition. Journal of Plant Nutrition, 19, 115–127.

Navarro, J. M., Matinez, V., Cerda, A., & Botella, M. A. (2000).

Effect of salinity 9 calcium interaction on cation balance

in melon plants grown under two regimes of orthophos-

phate. Journal of Plant Nutrition, 21, 991–1006.

Nikkarinen, M., & Mertanen, E. (2004). Impact of geological

origin on trace element composition of edible mushrooms.

Journal of Food Composition and Analysis, 17, 301–310.

Ozcan, M. (1999). Pickling and storage of caperberries (Cap-

paris spp.). European Food Research and Technology,

208, 379–382.

Pepi, S., Grisenti, P., Sansone, L., Chicca, M., & Vaccaro, C.

(2017a). Chemical elements as fingerprints of geographical

origin in cultivars of Vitis vinifera L. raised on the same

SO4 rootstock. Environmental Science and Pollution

Research. https://doi.org/10.1007/s11356-017-0443-y.

Pepi, S., Sansone, L., Chicca, M., Marrocchino, E., & Vaccaro,

C. (2016). Distribution of rare earth elements in soil and

grape berries of Vitis vinifera cv. ‘‘Glera’’. Environmental

Monitoring and Assessment, 188, 477.

Pepi, S., Sansone, L., Chicca, M., & Vaccaro, C. (2017b).

Relationship among geochemical elements in soil and

grapes as terroir fingerprintings in Vitis vinifera L. cv.

‘‘Glera’’. Chemie Der Erde-Geochemistry, 77, 121–130.

Pepi, S., & Vaccaro, C. (2017). Geochemical fingerprints of

‘‘Prosecco’’ wine based on major and trace elements. En-

vironmental Geochemistry and Health. https://doi.org/10.

1007/s10653-017-0029-0.

Pii, Y., Zamboni, A., Dal Santo, S., Pezzotti, M., Varanini, Z., &

Pandolfini, T. (2017). Prospect on ionomic signatures for

the classification of grapevine berries according to their

geographical origin. Frontiers and Plant Science, 8, 1–7.

Protano, G., & Rossi, S. (2014). Relationship between soil

geochemistry and grape composition in Tuscany (Italy).

Journal of Soil Science and Plant Nutrition, 177, 500–508.

Rencher, A. C. (2002). Methods of multivariate analysis (2nd

ed.). New York: Wiley.

Rivera, D., Inocencio, C., Obon, C., & Alcaraz, F. (2003).

Review of food and medicinal uses of Capparis L. sub-

genus Capparis (Capparidaceae). Economic Botany, 57,

34–515.

Sozzi, G. O. (2001). Caper bush: Botany and horticulture.

Horticultural Reviews, 27, 88–125.

Sozzi, G. O., Peter, K. V., Nirmal Babu, K., & Divakaran, M.

(2012). Capers and caperberries. In K. V. Peter (Ed.),

Handbook of herbs and spices. Philadelphia: Woodhead

Publishing Limited.

Swoboda, S., Brunner, M., Boulyga, S. F., Galler, P., Horacek,

M., & Prohaska, T. (2008). Identification of Marchfeld

asparagus using Sr isotope ratio measurements by MC-

ICP-MS. Analytical and Bioanalytical Chemistry, 390,

487–494.

Thiel, G., Geisler, G., Blechschmidt, I., & Danzer, K. (2004).

Determination of trace elements in wines and classification

according to their provenance. Analytical and Bioanalyti-

cal Chemistry, 378, 1630–1636.

Troeh, F. R., & Thompson, L. M. (1993). Soils and soil fertility

(6th ed.). New York: Oxford University Press.

Van Leeuwen, C., & Seguin, G. (2006). The concept of terroir in

viticulture. Journal of Wine Research, 17, 1–10.

Versari, A., Laurie, F. V., Ricci, A., Laghi, L., & Parpinello, G.

P. (2014). Progress in authentication, typification and


123

https://doi.org/10.1007/s11356-017-0443-y

https://doi.org/10.1007/s10653-017-0029-0

https://doi.org/10.1007/s10653-017-0029-0

traceability of grapes and wines by chemometric approa-

ches. Food Research International, 60, 2–18.

Wilson, J. E. (1998). Terroir: The role of geology, climate, and

culture in the making of French Wines (Wine Wheels).

Hardcover.

Zeng, F., Tian, H., Wang, Z., An, Y., Gao, F., Zhang, L., et al.

(2003). Effect of rare element europium on amaranthin

synthesis in Amarathus caudatus seedlings. Biological

Trace Element Research, 93, 271–282.

Zhao, H., Guo, B., Wei, Y., Zhang, B., Sun, S., Zhang, L., et al.

(2011). Determining the geographic origin of wheat using

multielement analysis and multivariate statistics. Journal

of Agricultural and Food Chemistry, 59, 4397–4402.


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Geochemical caper fingerprints as a tool for geographical